Rhythmicity of neuronal oscillations delineates their cortical and spectral architecture

Neuronal oscillations are commonly analyzed with power spectral methods that quantify signal amplitude, but not rhythmicity or 'oscillatoriness' per se. Here we introduce a new approach, the phase-autocorrelation function (pACF), for direct quantification of rhythmicity. We applied pACF to human intracerebral stereo-electroencephalography (SEEG) and magnetoencephalography (MEG) data and uncovered a spectrally and anatomically fine-grained cortical architecture in the rhythmicity of single- and multi-frequency neuronal oscillations. Evidencing the functional significance of rhythmicity, we found it to be a prerequisite for long-range synchronization in resting-state networks and to be dynamically modulated during event-related processing. We also extended the pACF approach to measure 'burstiness' of oscillatory processes and characterized regions with stable and bursty oscillations. These findings show that rhythmicity is double-dissociable from amplitude and constitutes a functionally r..., We assembled a large database of human intracerebral stereoelectroencephalography (SEEG, N=61) and magnetoencephalography (MEG, N=52) recordings. We applied the Phase Autocorrelation Function (pACF) for such recordings to obtain the rhytmicity map of human cortex for both modalities. We also applied the time-resolved pACF to analyze the event-related visual Threshold-Stimulus Detection Task (TSDT) and build maps of responses for it. To compare other methods to the pACF we also computed Power Spectral Density (PSD) and Wavelet Amplitude Spectra for the resting-state recordings alongside with evoked and phase-reset responses for the TSDT data., , ## Usage notes

The data consists of Python numpy and pickle files and is grouped in the next directories:

1. anatomy
\* brain_anatomy.pickle - a file with brain surfaces required for visualization of brain maps
2. MEG\
Common MEG derivatives that are used across multiple figures. In includes pACF and PSD brain maps for the MEG data, wPLI for the resting-state cohort, surrogate pACF level, parcel names mapping between yeo7 and yeo17 parcellations.
\* 7_to_17_400.csv \ A table with parcel names mapping between yeo7 and yeo17 parcellations
\* meg_noise_level.npy \ pACF noise level for MEG data
\* meg_pac_results_full.pickle \ pACF brain map for the resting-state MEG cohort
\* meg_pac_with_statistics_wpli.pickle \ pACF with wPLI and PSD statistics for the MEG cohort
3. SEEG\
Common SEEG derivatives that are used across multiple figures. In includes pACF brain maps for the SEEG data, surrogate pACF level, number of contacts per parcel and the parcellation ad...